An interior-point $l_{\frac{1}{2}}$-penalty method for inequality constrained nonlinear optimization

Boshi  Tian; Xiaoqi Yang; Kaiwen  Meng

doi:10.3934/jimo.2016.12.949

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July 2016, 12(3): 949-973. doi: 10.3934/jimo.2016.12.949

An interior-point $l_{\frac{1}{2}}$-penalty method for inequality constrained nonlinear optimization

Boshi Tian ^1, , Xiaoqi Yang ^2, and Kaiwen Meng ^3,

1.	Business School, Hunan University, Changsha 410082, Hunan Province, China
2.	Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
3.	School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China

Received February 2014 Revised April 2015 Published September 2015

Abstract
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In this paper, we study inequality constrained nonlinear programming problems by virtue of an $\ell{\frac12}$-penalty function and a quadratic relaxation. Combining with an interior-point method, we propose an interior-point $\ell_{\frac12}$-penalty method. We introduce different kinds of constraint qualifications to establish the first-order necessary conditions for the quadratically relaxed problem. We apply the modified Newton method to a sequence of logarithmic barrier problems, and design some reliable algorithms. Moreover, we establish the global convergence results of the proposed method. We carry out numerical experiments on 266 inequality constrained optimization problems. Our numerical results show that the proposed method is competitive with some existing interior-point $\ell_1$-penalty methods in term of iteration numbers and better when comparing the values of the penalty parameter.

Keywords: quadratic relaxation, constraint qualification, Nonlinear programming, primal-dual interior-point method., lower-order penalty function.

Mathematics Subject Classification: Primary: 90C30; Secondary: 49M20, 90C5.

Citation: Boshi Tian, Xiaoqi Yang, Kaiwen Meng. An interior-point $l_{\frac{1}{2}}$-penalty method for inequality constrained nonlinear optimization. Journal of Industrial & Management Optimization, 2016, 12 (3) : 949-973. doi: 10.3934/jimo.2016.12.949

References:

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[7]	L. Chen and D. Goldfarb, On the Fast Local Convergence of Interior-point $l_2$-penalty Methods for Nonlinear Programming, Technical report, Columbia University, 2006. Google Scholar
[8]	X. J. Chen, Smoothing methods for nonsmooth, nonconvex minimization, Mathematical Programming, 134 (2012), 71-99. doi: 10.1007/s10107-012-0569-0. Google Scholar
[9]	A. R. Conn, N. I. M. Gould, D. Orban and P. L. Toint, A primal-dual trust-region algorithm for non-convex nonlinear programming, Mathematical Programming, 87 (2000), 215-249. doi: 10.1007/s101070050112. Google Scholar
[10]	F. E. Curtis, A penalty-interior-point algorithm for nonlinear constrained optimization, Mathematical Programming Computation, 4 (2012), 181-209. doi: 10.1007/s12532-012-0041-4. Google Scholar
[11]	E. D. Dolan and J. J. Moré, Benchmarking optimization software with performance profiles, Mathematical Programming, 91 (2002), 201-213. doi: 10.1007/s101070100263. Google Scholar
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[14]	R. Fletcher, Practical Methods of Optimization, Second edition. A Wiley-Interscience Publication, John Wiley & Sons, Ltd., Chichester, 1987. Google Scholar
[15]	P. E. Gill, W. Murray and M. H. Wright, Practical Optimization, Academic Press, 1981. Google Scholar
[16]	N. I. M. Gould, P. L. Toint and D. Orban, An Interior-point $l_1$-penalty Method for Nonlinear Optimization, Groupe d'études et de recherche en analyse des décisions, 2010. Google Scholar
[17]	M. Guignard, Generalized Kuhn-Tucker conditions for mathematical programming problems in a Banach space, SIAM Journal on Control, 7 (1969), 232-241. doi: 10.1137/0307016. Google Scholar
[18]	X. X. Huang and X. Q. Yang, Convergence analysis of a class of nonlinear penalization methods for constrained optimization via first-order necessary optimality conditions, Journal of Optimization Theory and Applications, 116 (2003), 311-332. doi: 10.1023/A:1022503820909. Google Scholar
[19]	X. X. Huang and X. Q. Yang, A unified augmented Lagrangian approach to duality and exact penalization, Mathematics of Operations Research, 28 (2003), 533-552. doi: 10.1287/moor.28.3.533.16395. Google Scholar
[20]	X. W. Liu and J. Sun, A robust primal-dual interior-point algorithm for nonlinear programs, SIAM Journal on Optimization, 14 (2004), 1163-1186. doi: 10.1137/S1052623402400641. Google Scholar
[21]	X. W. Liu and J. Sun, Global convergence analysis of line search interior-point methods for nonlinear programming without regularity assumptions, Journal of Optimization Theory and Applications, 125 (2005), 609-628. doi: 10.1007/s10957-005-2092-4. Google Scholar
[22]	Z. Q. Luo, J. S. Pang and D. Ralph, Mathematical Programs with Equilibrium Constraints, Cambridge University Press, 1996. doi: 10.1017/CBO9780511983658. Google Scholar
[23]	K. W. Meng, S. J. Li and X. Q. Yang, A robust SQP method based on a smoothing lower order penalty functions, Optimization, 58 (2009), 23-38. doi: 10.1080/02331930701761193. Google Scholar
[24]	K. W. Meng and X. Q. Yang, First- and second-order necessary conditions via exact penalty functions, Journal of Optimization Theory and Applications, 165 (2015), 720-752. doi: 10.1007/s10957-014-0664-x. Google Scholar
[25]	K. W. Meng and X. Q. Yang, Optimality conditions via exact penalty functions, SIAM Journal on Optimization, 20 (2010), 3208-3231. doi: 10.1137/090771016. Google Scholar
[26]	Z. Q. Meng, C. Y. Dang and X. Q. Yang, On the smoothing of the square-root exact penalty function for inequality constrained optimization, Computational Optimization and Applications, 35 (2006), 375-398. doi: 10.1007/s10589-006-8720-6. Google Scholar
[27]	M. Mongeau and A. Sartenaer, Automatic decrease of the penalty parameter in exact penalty function methods, European Journal of Operational Research, 83 (1995), 686-699. doi: 10.1016/0377-2217(93)E0339-Y. Google Scholar
[28]	A. S. Nemirovski and M. J. Todd, Interior-point methods for optimization, Acta Numerica, 17 (2008), 191-234. doi: 10.1017/S0962492906370018. Google Scholar
[29]	J. Nocedal and S. J. Wright, Numerical Optimization, Springer Verlag, 2006. Google Scholar
[30]	I. Pólik and T. Terlaky, Interior point methods for nonlinear optimization, Nonlinear optimization, Lecture Notes in Math., Springer, Berlin, 1989 (2010), 215-276. doi: 10.1007/978-3-642-11339-0_4. Google Scholar
[31]	R. T. Rockafellar and R. J. B. Wets, Variational Analysis, Springer-Verlag, Berlin, 1998. doi: 10.1007/978-3-642-02431-3. Google Scholar
[32]	A. M. Rubinov, B. M. Glover and X. Q. Yang, Decreasing functions with applications to penalization, SIAM Journal on Optimization, 10 (1999), 289-313. doi: 10.1137/S1052623497326095. Google Scholar
[33]	D. F. Shanno and R. J. Vanderbei, Interior-point methods for nonconvex nonlinear programming: Orderings and higher-order methods, Mathematical Programming, 87 (2000), 303-316. doi: 10.1007/s101070050116. Google Scholar
[34]	G. Still and M. Streng, Optimality conditions in smooth nonlinear programming, Journal of Optimization Theory and Applications, 90 (1996), 483-515. doi: 10.1007/BF02189792. Google Scholar
[35]	R. J. Vanderbei and D. F. Shanno, An interior-point algorithm for nonconvex nonlinear programming, Computational Optimization and Applications, 13 (1999), 231-252. doi: 10.1023/A:1008677427361. Google Scholar
[36]	A. Wächter and L. T. Biegler, On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, Mathematical Programming, 106 (2006), 25-57. doi: 10.1007/s10107-004-0559-y. Google Scholar
[37]	S. J. Wright, Primal-dual Interior-Point Methods, SIAM, 1987. doi: 10.1137/1.9781611971453. Google Scholar
[38]	Z. B. Xu, X. Y. Chang, F. M. Xu and H. Zhang, $L_{1/2}$ Regularization: A thresholding representation theory and a fast solver, Neural Networks and Learning Systems, IEEE Transactions on, 23 (2012), 1013-1027. Google Scholar
[39]	X. Q. Yang and Z. Q. Meng, Lagrange multipliers and calmness conditions of order $p$, Mathematics of Operations Research, 32 (2007), 95-101. doi: 10.1287/moor.1060.0217. Google Scholar
[40]	X. Q. Yang, Z. Q. Meng, X. X. Huang and G. T. Y. Pong, Smoothing nonlinear penalty functions for constrained optimization problems, Numerical Functional Analysis and Optimization, 24 (2003), 351-364. doi: 10.1081/NFA-120022928. Google Scholar
[41]	Y. Y. Ye, Interior Point Algorithms: Theory and Analysis, Wiley-Interscience, 1997. doi: 10.1002/9781118032701. Google Scholar

show all references

References:

[1]	H. Y. Benson, A. Sen and D. F. Shanno, Interior-point methods for nonconvex nonlinear programming: Convergence analysis and computational performance, http://rutcor.rutgers.edu/~shanno/converge5.pdf, 2009. Google Scholar
[2]	H. Y. Benson, D. F. Shanno and R. J. Vanderbei, Interior-point methods for nonconvex nonlinear programming: Jamming and numerical testing, Mathematical Programming, 99 (2004), 35-48. doi: 10.1007/s10107-003-0418-2. Google Scholar
[3]	R. H. Byrd, G. Lopez-Calva and J. Nocedal, A line search exact penalty method using steering rules, Mathematical Programming, 133 (2012), 39-73. doi: 10.1007/s10107-010-0408-0. Google Scholar
[4]	R. H. Byrd, J. Nocedal and R. A. Waltz, Steering exact penalty methods for nonlinear programming, Optimization Methods and Software, 23 (2008), 197-213. doi: 10.1080/10556780701394169. Google Scholar
[5]	R. Chartrand and V. Staneva, Restricted isometry properties and nonconvex compressive sensing, Inverse Problems, 24 (2008), 035020, 14 pp. doi: 10.1088/0266-5611/24/3/035020. Google Scholar
[6]	L. Chen and D. Goldfarb, Interior-point $l_2$-penalty methods for nonlinear programming with strong global convergence properties, Mathematical Programming, 108 (2006), 1-36. doi: 10.1007/s10107-005-0701-5. Google Scholar
[7]	L. Chen and D. Goldfarb, On the Fast Local Convergence of Interior-point $l_2$-penalty Methods for Nonlinear Programming, Technical report, Columbia University, 2006. Google Scholar
[8]	X. J. Chen, Smoothing methods for nonsmooth, nonconvex minimization, Mathematical Programming, 134 (2012), 71-99. doi: 10.1007/s10107-012-0569-0. Google Scholar
[9]	A. R. Conn, N. I. M. Gould, D. Orban and P. L. Toint, A primal-dual trust-region algorithm for non-convex nonlinear programming, Mathematical Programming, 87 (2000), 215-249. doi: 10.1007/s101070050112. Google Scholar
[10]	F. E. Curtis, A penalty-interior-point algorithm for nonlinear constrained optimization, Mathematical Programming Computation, 4 (2012), 181-209. doi: 10.1007/s12532-012-0041-4. Google Scholar
[11]	E. D. Dolan and J. J. Moré, Benchmarking optimization software with performance profiles, Mathematical Programming, 91 (2002), 201-213. doi: 10.1007/s101070100263. Google Scholar
[12]	C. Durazzi, On the Newton interior-point method for nonlinear programming problems, Journal of Optimization Theory and Applications, 104 (2000), 73-90. doi: 10.1023/A:1004624721836. Google Scholar
[13]	A. S. El-Bakry, R. A. Tapia, T. Tsuchiya and Y. Zhang, On the formulation and theory of the Newton interior-point method for nonlinear programming, Journal of Optimization Theory and Applications, 89 (1996), 507-541. doi: 10.1007/BF02275347. Google Scholar
[14]	R. Fletcher, Practical Methods of Optimization, Second edition. A Wiley-Interscience Publication, John Wiley & Sons, Ltd., Chichester, 1987. Google Scholar
[15]	P. E. Gill, W. Murray and M. H. Wright, Practical Optimization, Academic Press, 1981. Google Scholar
[16]	N. I. M. Gould, P. L. Toint and D. Orban, An Interior-point $l_1$-penalty Method for Nonlinear Optimization, Groupe d'études et de recherche en analyse des décisions, 2010. Google Scholar
[17]	M. Guignard, Generalized Kuhn-Tucker conditions for mathematical programming problems in a Banach space, SIAM Journal on Control, 7 (1969), 232-241. doi: 10.1137/0307016. Google Scholar
[18]	X. X. Huang and X. Q. Yang, Convergence analysis of a class of nonlinear penalization methods for constrained optimization via first-order necessary optimality conditions, Journal of Optimization Theory and Applications, 116 (2003), 311-332. doi: 10.1023/A:1022503820909. Google Scholar
[19]	X. X. Huang and X. Q. Yang, A unified augmented Lagrangian approach to duality and exact penalization, Mathematics of Operations Research, 28 (2003), 533-552. doi: 10.1287/moor.28.3.533.16395. Google Scholar
[20]	X. W. Liu and J. Sun, A robust primal-dual interior-point algorithm for nonlinear programs, SIAM Journal on Optimization, 14 (2004), 1163-1186. doi: 10.1137/S1052623402400641. Google Scholar
[21]	X. W. Liu and J. Sun, Global convergence analysis of line search interior-point methods for nonlinear programming without regularity assumptions, Journal of Optimization Theory and Applications, 125 (2005), 609-628. doi: 10.1007/s10957-005-2092-4. Google Scholar
[22]	Z. Q. Luo, J. S. Pang and D. Ralph, Mathematical Programs with Equilibrium Constraints, Cambridge University Press, 1996. doi: 10.1017/CBO9780511983658. Google Scholar
[23]	K. W. Meng, S. J. Li and X. Q. Yang, A robust SQP method based on a smoothing lower order penalty functions, Optimization, 58 (2009), 23-38. doi: 10.1080/02331930701761193. Google Scholar
[24]	K. W. Meng and X. Q. Yang, First- and second-order necessary conditions via exact penalty functions, Journal of Optimization Theory and Applications, 165 (2015), 720-752. doi: 10.1007/s10957-014-0664-x. Google Scholar
[25]	K. W. Meng and X. Q. Yang, Optimality conditions via exact penalty functions, SIAM Journal on Optimization, 20 (2010), 3208-3231. doi: 10.1137/090771016. Google Scholar
[26]	Z. Q. Meng, C. Y. Dang and X. Q. Yang, On the smoothing of the square-root exact penalty function for inequality constrained optimization, Computational Optimization and Applications, 35 (2006), 375-398. doi: 10.1007/s10589-006-8720-6. Google Scholar
[27]	M. Mongeau and A. Sartenaer, Automatic decrease of the penalty parameter in exact penalty function methods, European Journal of Operational Research, 83 (1995), 686-699. doi: 10.1016/0377-2217(93)E0339-Y. Google Scholar
[28]	A. S. Nemirovski and M. J. Todd, Interior-point methods for optimization, Acta Numerica, 17 (2008), 191-234. doi: 10.1017/S0962492906370018. Google Scholar
[29]	J. Nocedal and S. J. Wright, Numerical Optimization, Springer Verlag, 2006. Google Scholar
[30]	I. Pólik and T. Terlaky, Interior point methods for nonlinear optimization, Nonlinear optimization, Lecture Notes in Math., Springer, Berlin, 1989 (2010), 215-276. doi: 10.1007/978-3-642-11339-0_4. Google Scholar
[31]	R. T. Rockafellar and R. J. B. Wets, Variational Analysis, Springer-Verlag, Berlin, 1998. doi: 10.1007/978-3-642-02431-3. Google Scholar
[32]	A. M. Rubinov, B. M. Glover and X. Q. Yang, Decreasing functions with applications to penalization, SIAM Journal on Optimization, 10 (1999), 289-313. doi: 10.1137/S1052623497326095. Google Scholar
[33]	D. F. Shanno and R. J. Vanderbei, Interior-point methods for nonconvex nonlinear programming: Orderings and higher-order methods, Mathematical Programming, 87 (2000), 303-316. doi: 10.1007/s101070050116. Google Scholar
[34]	G. Still and M. Streng, Optimality conditions in smooth nonlinear programming, Journal of Optimization Theory and Applications, 90 (1996), 483-515. doi: 10.1007/BF02189792. Google Scholar
[35]	R. J. Vanderbei and D. F. Shanno, An interior-point algorithm for nonconvex nonlinear programming, Computational Optimization and Applications, 13 (1999), 231-252. doi: 10.1023/A:1008677427361. Google Scholar
[36]	A. Wächter and L. T. Biegler, On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, Mathematical Programming, 106 (2006), 25-57. doi: 10.1007/s10107-004-0559-y. Google Scholar
[37]	S. J. Wright, Primal-dual Interior-Point Methods, SIAM, 1987. doi: 10.1137/1.9781611971453. Google Scholar
[38]	Z. B. Xu, X. Y. Chang, F. M. Xu and H. Zhang, $L_{1/2}$ Regularization: A thresholding representation theory and a fast solver, Neural Networks and Learning Systems, IEEE Transactions on, 23 (2012), 1013-1027. Google Scholar
[39]	X. Q. Yang and Z. Q. Meng, Lagrange multipliers and calmness conditions of order $p$, Mathematics of Operations Research, 32 (2007), 95-101. doi: 10.1287/moor.1060.0217. Google Scholar
[40]	X. Q. Yang, Z. Q. Meng, X. X. Huang and G. T. Y. Pong, Smoothing nonlinear penalty functions for constrained optimization problems, Numerical Functional Analysis and Optimization, 24 (2003), 351-364. doi: 10.1081/NFA-120022928. Google Scholar
[41]	Y. Y. Ye, Interior Point Algorithms: Theory and Analysis, Wiley-Interscience, 1997. doi: 10.1002/9781118032701. Google Scholar

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American Institute of Mathematical Sciences

An interior-point $l_{\frac{1}{2}}$-penalty method for inequality constrained nonlinear optimization

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An interior-point $l_{\frac{1}{2}}$-penalty method for inequality constrained nonlinear optimization

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